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from glob import iglob
from db import dbengine
from pandas.tseries.offsets import MonthEnd
from yieldcurve import YC
import os
import pandas as pd
import datetime
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import pandas.tseries.offsets as off
def get_monthly_pnl(group_by = ['identifier']):
sql_string = "SELECT * FROM pnl_reports"
df_pnl = pd.read_sql_query(sql_string, dbengine('dawndb'), parse_dates=['date'],
index_col=['date'])
df_pnl['identifier'] = df_pnl.invid.str.replace("_A$", "")
pnl_cols = ['bookunrealmtm', 'bookrealmtm', 'bookrealincome', 'bookunrealincome', 'totalbookpl']
monthend_pnl = df_pnl.groupby(pd.Grouper(freq='M'), group_keys=False).apply(lambda df: df.loc[df.index[-1]])
return monthend_pnl.groupby(['date'] + group_by)[['mtd' + col for col in pnl_cols]].sum()
def get_portfolio(report_date = None):
if report_date is not None:
sql_string = "SELECT * FROM valuation_reports where periodenddate = %s"
df = pd.read_sql_query(sql_string, dbengine('dawndb'), parse_dates=['periodenddate'],
index_col=['periodenddate'], params=[report_date,])
else:
sql_string = "SELECT * FROM valuation_reports"
df = pd.read_sql_query(sql_string, dbengine('dawndb'), parse_dates=['periodenddate'],
index_col=['periodenddate'])
df['identifier'] = df.invid.str.replace("_A$", "")
return df
def curr_port_PNL(date = datetime.date.today(), asset_class='Subprime'):
date = (date - off.MonthEnd(1)).date()
sql_string = "SELECT * FROM risk_positions(%s, %s) WHERE notional > 0"
df_positions = pd.read_sql_query(sql_string, dbengine('dawndb'), params=[date, asset_class])
df_pnl = get_monthly_pnl()[:date]
df_all = df_positions.merge(df_pnl.groupby('identifier').sum().reset_index(), on=['identifier'])
return df_all
def trade_performance():
sql_string = "SELECT * FROM bonds"
df_trades = pd.read_sql_query(sql_string, dbengine('dawndb'), parse_dates={'lastupdate': {'utc': True}, 'trade_date': {}, 'settle_date': {}})
df_trades = df_trades[df_trades['asset_class'] == 'Subprime']
df_pnl = get_monthly_pnl()
df_sell = df_trades[df_trades.buysell == False].groupby('identifier').last().reset_index()
df_sell.identifier = df_sell.identifier.str[:9]
df_sell['trade_pnl_date'] = df_sell.trade_date + off.MonthEnd(0)
df_buy = df_trades[df_trades.buysell == True].groupby('identifier').last().reset_index()
df_all = df_sell.merge(df_pnl.groupby('identifier').sum().reset_index(), on=['identifier'])
df_all = df_all.merge(df_pnl.reset_index()[['date', 'identifier', 'mtdtotalbookpl']],
left_on=['trade_pnl_date', 'identifier'],
right_on=['date', 'identifier'],
suffixes=('', '_at_trade_month'))
df_all = df_all.drop(['date', 'trade_pnl_date'], axis=1)
#now build up the table
g = df_buy.groupby('identifier').sum()
init_inv = g.principal_payment + g.accrued_payment
init_inv.name = 'initialinvestment'
first_buy_date = df_buy.groupby('identifier').first().trade_date
first_buy_date.name = 'firstbuydate'
df_all = df_all.join(init_inv, on='identifier')
df_all = df_all.join(first_buy_date, on='identifier')
df_all['percent_gain'] = df_all.mtdtotalbookpl / df_all.initialinvestment
df_all['days_held'] = df_all.trade_date - df_all.firstbuydate
df_all = df_all.sort_values('trade_date', ascending=False)
return df_all
def get_net_navs():
sql_string = "SELECT * FROM valuation_reports"
df_val = pd.read_sql_query(sql_string, dbengine('dawndb'), parse_dates=['periodenddate'])
nav = df_val[df_val.fund == 'SERCGMAST'].groupby('periodenddate')['endbooknav'].sum()
nav = nav.resample('M').last()
df = pd.read_csv('/home/serenitas/edwin/Python/subscription_fee_data.csv', parse_dates=['date'], index_col =['date'])
df.index = df.index.to_period('M').to_timestamp('M')
df = df.join(nav)
df['begbooknav'] = (df.endbooknav + df.net_flow).shift(1)
df.at[('2013-01-31', 'begbooknav')] = 12500000
return df
def shift_cash(date, amount, df, strat):
nav = get_net_navs()
df.loc[date, strat] = df.loc[date, strat] - amount/nav.loc[date].endbooknav
df.loc[date,'Cash'] = df.loc[date, 'Cash'] + amount/nav.loc[date].endbooknav
return df
def calc_trade_performance_stats():
df = trade_performance().set_index('trade_date')
df.days_held = df.days_held.dt.days
df['winners'] = df.apply(lambda df: True if df.percent_gain > 0 else False, axis = 1)
df['curr_face'] = df.principal_payment/(df.price/100)
index = ['All', '2017', '2016', '2015', '2014', '2013']
results = pd.DataFrame(index = index)
win_per = len(df[df.winners].index)/len(df)
loss_per = 1- win_per
temp = {}
temp1 = {}
for x, df1 in df.groupby('winners'):
for y, df2 in df1.groupby(pd.Grouper(freq='A')):
import pdb; pdb.set_trace()
y = y.date().year
results.loc[y] = df2[df2.days_held.notnull()].mean()[['curr_face','initialinvestment', 'days_held']]
df[df.days_held.notnull()]['days_held'].groupby(pd.Grouper(freq='A')).mean()
def get_rmbs_pos_df(date = None):
engine = dbengine('dawndb')
end_date = pd.datetime.today() - MonthEnd(1)
if date is not None:
date = date + MonthEnd(0)
df = get_portfolio(date)
df = df.sort_index().loc[:end_date]
df = df[(df.port == 'MORTGAGES') &
(df.endbookmv > 0) &
(df['invid'].str.len() == 9)]
sql_string = "SELECT distinct timestamp FROM priced where normalization = 'current_notional'"
timestamps = pd.read_sql_query(sql_string, engine)
df = df[['endbooknav', 'endlocalmarketprice', 'identifier']]
calc_df = pd.DataFrame()
for d, g in df.groupby(pd.Grouper(freq='M')):
model_date = pd.to_datetime(timestamps[timestamps.timestamp <= d+off.DateOffset(days=1)].max()[0]).date()
yc = YC(evaluation_date=model_date)
libor = float(yc.zero_rate(.125))
if d > pd.datetime(2017, 9, 30):
model_id_sql_string = "SELECT * FROM latest_sim(%s)"
model_id = pd.read_sql_query(model_id_sql_string, engine, params=[model_date])
model_id = model_id.loc[0][0]
#special case
if model_date == pd.datetime(2017, 10, 27).date():
model_id = 4
sql_string = """
SELECT date(timestamp) as timestamp, cusip, model_version, pv, moddur, delta_yield, delta_ir
FROM priced where date(timestamp) = %s
and normalization ='current_notional'
and model_version <> 2
and model_id_sub = %s"""
params_list = [model_date, model_id]
else:
sql_string = """
SELECT date(timestamp) as timestamp, cusip, model_version, pv, moddur, delta_yield, delta_ir
FROM priced where date(timestamp) = %s
and model_version <> 2
and normalization ='current_notional'"""
params_list = [model_date]
model = pd.read_sql_query(sql_string, engine, parse_dates=['timestamp'],
params=params_list)
comb_g = g.loc[d].groupby('identifier').agg({'endbooknav': np.sum,
'endlocalmarketprice': np.mean})
model = pd.merge(comb_g, model, left_on = 'identifier', right_on='cusip')
positions = model.set_index(['cusip', 'model_version']).unstack(1).dropna()
positions = positions[positions.pv.iloc[:,0] != 0]
v1 = positions.xs(1, level='model_version', axis=1)
v3 = positions.xs(3, level='model_version', axis=1)
v3 = v3.assign(curr_ntl = v3.endbooknav/v3.endlocalmarketprice *100)
v3 = v3.assign(b_yield = v3.moddur.apply(lambda x:
float(yc.zero_rate(x)) - libor))
v3.b_yield += np.minimum((v1.pv / v1.endlocalmarketprice * 100)
** (1/v1.moddur) - 1, 10).dropna()
v3.delta_yield = v3.delta_yield * (v3.endlocalmarketprice/100)/ v3.pv * v3.curr_ntl
v3.delta_ir = v3.delta_ir * np.minimum(1, 1/v3.moddur) * (v3.endlocalmarketprice/100)/ v3.pv * v3.curr_ntl
calc_df = calc_df.append(v3)
return calc_df.reset_index().set_index('timestamp').sort_index()
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